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A STATISTICAL APPROACH TO PREDICT THE STERILIZATION VALUE FOR CANNED OLIVES

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Abstract:

ABSTRACT

It is essential that sterilization values be calculated through measurements of temperature from the coldest point of the can during thermal sterilization. However, in practice, the taking of such measurements in an operating food plant can be quite difficult because of time restrictions, and the number of thermocouples and measurements required. For this reason, it is necessary to develop a more convenient way of estimating F values that uses experimental methods through multiple regression models. The distribution of F values and the correlation between the retort temperature and the cold spot temperature of the can were taken into consideration using statistical methods. The estimated results were calculated with very low SD.

It was found that there was a close relation between temperatures of retort and cold spot with a high correlation ratio, and thus it could be concluded that it is possible to use retort temperature in the estimation of F values, statistically.

The experimental and estimated mean averages of F values were calculated as 13.59 and 13.59 for a 0.5-kg can, 14.15 and 14.16 for a 1-kg can, and 8.22 and 8.22 for a 5-kg can. A high correlation was found between the experimental and estimated F values (0.91 for a 0.5-kg can, 0.86 for a 1-kg can and 0.96 for a 5-kg can; P < 0.001). It was also noted that an inversely proportional relation exists between the size of the can and the F value. PRACTICAL APPLICATIONS

In addition to measuring F values through experimentation, a more convenient way of estimating F values is sought using other methods. For this purpose, the relationship between the retort temperature and cold spot temperature of a can was investigated making use of statistical data.

It was found that, according to the statistical findings, it is possible to estimate F values by using the retort temperature.

A new statistical approach to estimating F values was developed, investigating previous statistical methods, such as cumulative histograms, Laplace distribution, Kolmogorov–Simirnov test, etc.

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1745-4549.2007.00197.x

Affiliations: 1: Department of Agricultural Economics 2: Department of Food EngineeringFaculty of AgricultureUludag UniversityBursa, Turkey

Publication date: 2008-06-01

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